Advances in analytical instruments, examples of which can include microscopes, spectrometers, and diffractometers, have dramatically improved the quantity and the quality of data obtainable by the instruments. For example, electron microscopy is a powerful tool that can provide high spatial and temporal resolution of nanoscale objects and processes. However, practically speaking, the quantity and quality of information obtainable is now often limited by observer effects on the specimen, the data-handling capacity of associated computational systems, and/or acquisition times that are too long. In the example of electron microscopes, the long dwell time, the high electron beam current, and/or the large amounts of generated data associated with delivering such high resolution and sensitivity can be problematic.
Application of computational imaging techniques, including compressive sensing and inpainting, to reconstruct representations of fully-sampled information from sparse datasets obtained by sub-sampling a specimen can minimize observer effects, acquisition times, and/or data-handling burdens. However, utilization of such techniques has yet to be optimized. Accordingly, there exists a need for improvements in sub-sampling techniques and systems to enable fast and efficient acquisition of measurements in analytical instruments.